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Record W2778671969 · doi:10.1097/mcp.0000000000000460

Self-management strategies in chronic obstructive pulmonary disease

2017· review· en· W2778671969 on OpenAlexaff
Míriam Barrecheguren, Jean Bourbeau

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineSelf-managementPsychological interventionQuality of life (healthcare)Disease managementIntervention (counseling)Goal settingMEDLINEHealth careDiseaseIntensive care medicinePhysical therapyNursingPsychology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Self-management has gained increased relevance in the management of chronic obstructive pulmonary disease patients. The heterogeneity in self-management interventions has complicated the development of recommendations for clinical practice. In this review, we present the latest findings regarding conceptual definition, effectiveness of self-management interventions and self-management strategies in chronic obstructive pulmonary disease as a first step toward personalized medicine: what, how and to whom? RECENT FINDINGS: Self-management interventions have shown benefits in improving health-related quality of life and reducing hospital admissions. Favorable outcomes can only be achieved if patients have an ultimate goal, that is their desired achievements in their life. In the continuum of care, the components of the self-management program will vary to adapt to the condition of the patient (disease severity, comorbidities) and to factors such as patient motivation, confidence (self-efficacy), access to health care, family and social support. A combination of education, case management and patient-centric action plan has shown the best chance of success. SUMMARY: The individual patient's needs, own preferences and personal goals should inform the design of any intervention with a behavioral component. A continuous loop process has to be implemented to constantly assess what work and does not work, aiming at achieving the desired outcomes for a given patient.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.428
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2017
Admission routes1
Has abstractyes

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